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HEATS: Heterogeneity- and Energy-Aware Task-based Scheduling

Isabelly Rocha, Christian Göttel, Pascal Felber, Marcelo Pasin, Romain Rouvoy, Valerio Schiavoni
University of Neuchatel, Switzerland
arXiv:1906.11321 [cs.DC], (26 Jun 2019)

@misc{rocha2019heats,

   title={HEATS: Heterogeneity- and Energy-Aware Task-based Scheduling},

   author={Isabelly Rocha and Christian Göttel and Pascal Felber and Marcelo Pasin and Romain Rouvoy and Valerio Schiavoni},

   year={2019},

   eprint={1906.11321},

   archivePrefix={arXiv},

   primaryClass={cs.DC}

}

Cloud providers usually offer diverse types of hardware for their users. Customers exploit this option to deploy cloud instances featuring GPUs, FPGAs, architectures other than x86 (e.g., ARM, IBM Power8), or featuring certain specific extensions (e.g, Intel SGX). We consider in this work the instances used by customers to deploy containers, nowadays the de facto standard for micro-services, or to execute computing tasks. In doing so, the underlying container orchestrator (e.g., Kubernetes) should be designed so as to take into account and exploit this hardware diversity. In addition, besides the feature range provided by different machines, there is an often overlooked diversity in the energy requirements introduced by hardware heterogeneity, which is simply ignored by default container orchestrator’s placement strategies. We introduce HEATS, a new task-oriented and energy-aware orchestrator for containerized applications targeting heterogeneous clusters. HEATS allows customers to trade performance vs. energy requirements. Our system first learns the performance and energy features of the physical hosts. Then, it monitors the execution of tasks on the hosts and opportunistically migrates them onto different cluster nodes to match the customer-required deployment trade-offs. Our HEATS prototype is implemented within Google’s Kubernetes. The evaluation with synthetic traces in our cluster indicate that our approach can yield considerable energy savings (up to 8.5%) and only marginally affect the overall runtime of deployed tasks (by at most 7%). HEATS is released as open-source.
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